Achieving synchronization in an orthogonal time frequency space signal receiver

ABSTRACT

Methods, systems and device for achieving synchronization in an orthogonal time frequency space (OTFS) signal receiver are described. An exemplary signal reception technique includes receiving an OTFS modulated wireless signal comprising pilot signal transmissions interspersed with data transmissions, calculating autocorrelation of the wireless signal using the wireless signal and a delayed version of the wireless signal that is delayed by a pre-determined delay, thereby generating an autocorrelation output, processing the autocorrelation filter through a moving average filter to produce a fine timing signal. Another exemplary signal reception technique includes receiving an OTFS modulated wireless signal comprising pilot signal transmissions interspersed with data transmissions, performing an initial automatic gain correction of the received OTFS wireless signal by peak detection and using clipping information, performing coarse automatic gain correction on results of a received and initial automatic gain control (AGC)-corrected signal.

CROSS-REFERENCE TO RELATED APPLICATION

This patent document claims priority to and benefits of U.S. ProvisionalPatent Application No. 62/559,398 entitled “ACHIEVING SYNCHRONIZATION INAN ORTHOGONAL TIME FREQUENCY SPACE SIGNAL RECEIVER” and filed on 15 Sep.2017. The entire content of the aforementioned patent application isincorporated by reference as part of the disclosure of this patentdocument.

TECHNICAL FIELD

The present document relates to wireless communication, and moreparticularly, synchronization at a receiver.

BACKGROUND

Due to an explosive growth in the number of wireless user devices andthe amount of wireless data that these devices can generate or consume,current wireless communication networks are fast running out ofbandwidth to accommodate such a high growth in data traffic and providehigh quality of service to users.

Various efforts are underway in the telecommunication industry to comeup with next generation of wireless technologies that can keep up withthe demand on performance of wireless devices and networks.

SUMMARY

This document discloses techniques for receiver-side processing in whichsignals modulated using orthogonal time frequency space (OTFS)modulations are received and processed to extract modulated data.

In one example aspect, a signal reception method is disclosed. Themethod includes receiving an OTFS modulated wireless signal comprisingpilot signal transmissions interspersed with data transmissions,calculating autocorrelation of the wireless signal using the wirelesssignal and a delayed version of the wireless signal that is delayed by apre-determined delay, thereby generating an autocorrelation output,processing the autocorrelation filter through a moving average filter toproduce a fine timing signal, and performing synchronization with thewireless signal using the fine timing signal.

In another aspect a signal reception method is disclosed. The methodincludes receiving an OTFS modulated wireless signal comprising pilotsignal transmissions interspersed with data transmissions, performing aninitial automatic gain correction of the received OTFS wireless signalby peak detection and using clipping information, performing coarseautomatic gain correction on results of a received and initial automaticgain control (AGC)-corrected signal.

In another example aspect, a wireless communication apparatus thatimplements the above-described method is disclosed.

In yet another example aspect, the method may be embodied asprocessor-executable code and may be stored on a computer-readableprogram medium.

These, and other, features are described in this document.

DESCRIPTION OF THE DRAWINGS

Drawings described herein are used to provide a further understandingand constitute a part of this application. Example embodiments andillustrations thereof are used to explain the technology rather thanlimiting its scope.

FIG. 1 shows an example communication network.

FIG. 2 shows an example of multiplexing data and pilot symbols.

FIG. 3 shows an example structure of a pilot frame.

FIG. 4 shows an example structure of a data frame.

FIG. 5 is a block diagram highlighting examples of different blocks inreceiver synchronization, their inter-connections and their connectionto the receiver front end.

FIG. 6 shows a flowchart for signal processing performed in one exampleimplementation of receiver synchronization and AGC circuitry/algorithm.

FIG. 7 shows a block diagram of an example implementation ofsynchronization highlighting the top level control aspects.

FIG. 8 shows an example of the Front End (RF to Base Band) processingchain, highlighting Step 1 (the initial gain adjustment) of AGCdescribed in FIG. 6.

FIG. 9 shows an example of Step 2 (AGC coarse gain control) of FIG. 6.

FIG. 10 shows an example of a circuit for calculating autocorrelation ofpilots that are N (one OTFS frame) samples away.

FIG. 11 shows example waveforms after auto-correlation and movingaverage (MA) filtering, depicting an example where correctly designedmoving average filter entails a single peak irrespective of theunderlying channel.

FIG. 12 shows an example of moving average filter design criteria.

FIG. 13 is a graph of an example Matlab output of an autocorrelator.

FIG. 14 shows a zoomed view of an example graph of output of anautocorrelator.

FIG. 15 shows a zoomed view of an example graph of a moving averageoutput.

FIG. 16 shows a typical output from the MA-filter in a noisyenvironment.

FIG. 17 shows an example graph of a series of pilot autocorrelationpeaks and their positions. In some embodiments, this information is usedfor initial Carrier Frequency Offset estimation (e.g., Step 3 in FIG.6).

FIG. 18 shows an example of a coarse frequency and OTFS frame detectionscheme.

FIG. 19 shows an example of calculating correlation in transmissionswith a cyclic prefix and a cyclic suffix.

FIG. 20 shows an example of a circuit for calculating autocorrelationbetween the data and its CP which are separated by N samples.

FIG. 21 shows similar waveforms as in FIG. 11, for the case of CP/CScorrelations and corresponding moving average filtering.

FIG. 22 shows the relative positions of correlation peak, boundaries ofpilot frame and data frame for the example relation described for somedisclosed embodiments.

FIG. 23 an example scheme for fine frame boundary detection usingdemodulated channel response (e.g., Step 6 in FIG. 6).

FIG. 24 shows an exemplary flowchart for correcting the symbol timingoffset (e.g., Step 7 in FIG. 6).

FIG. 25 shows an example scheme for band-edge timing recovery.

FIG. 26 shows examples of band-pass filter design and the filterresponse.

FIG. 27 shows an example scheme for phase-locked loop tracking usingpilot autocorrelation peaks.

FIG. 28 shows an example of fine gain adjustment using pilotautocorrelation peak power.

FIGS. 29A, 29B and 29C show flowcharts for different portions of anexemplary implementation for AGC and synchronization.

FIG. 30 shows an example implementation of a correlator in hardware forthe Pilot and CP/CS portions of a frame.

FIG. 31 shows an example state machine implementation of a peakdetector.

FIG. 32 is a flowchart of an example of a wireless signal receptionmethod.

FIG. 33 is a flowchart of an example of a wireless signal receptionmethod.

FIG. 34 shows an example of a wireless transceiver apparatus.

FIG. 35 is an illustration of an example of fractional timing offseterror estimation.

DETAILED DESCRIPTION

To make the purposes, technical solutions and advantages of thisdisclosure more apparent, various embodiments are described in detailbelow with reference to the drawings. Unless otherwise noted,embodiments and features in embodiments of the present document may becombined with each other.

Section headings are used in the present document to improve readabilityof the description and do not, in any way, limit the discussion to therespective sections only.

In recent years, to meet the increased demand of ever higher datathroughput many new techniques have been introduced in wirelesscommunications. For example, the amount of bandwidth, measured as atotal number or as a number of bits per Hertz per second number, hasgrown steadily over years in prevalent communication standards such asthe Long Term Evolution (LTE). This trend is expected to grow even moredue to the explosion of smartphones and multimedia streaming services.

Recently, a modulation scheme called orthogonal time frequency space(OTFS), has been proposed for wireless data transmissions. An OTFStransmitter may generate transmission signals by modulating the incomingbit stream into OTFS frames. An OTFS receiver may be used to receiveOTFS modulated signals and recover data bits from the signal.

The OTFS receiver should reliably and accurately (a) estimate thereceiver gain required and apply it through RF and base band (bothanalog and digital) stages such that the digital sections of the OTFSreceiver obtain samples with maximum fidelity (this is sometimes calledautomatic gain control or AGC) and (b) acquire synchronization in thepresence of channels with a multitude of reflectors that has a range ofdelay and Doppler characteristics.

This document provides the requirements for AGC and synchronization andoutlines a technical approach to meet these requirements. In generalterms, both the AGC and synchronization is achieved by multiple stagesof intertwined processing, including the computation of auto-correlationfollowed by moving average filtering to perform initial, coarse and fineAGC and coarse synchronization and fine synchronization. These, andother aspects, are described in the present document.

Section headings are used only to facilitate readability and are notintended to limit the embodiments and technology described in eachsection only to that section.

FIG. 1 shows an example communication network 100 in which the disclosedtechnologies can be implemented. The network 100 may include a basestation transmitter that transmits wireless signals s(t) (downlinksignals) to one or more User Equipment (UE) receivers 102, the receivedsignal being denoted as r(t), which may be located in a variety oflocations, including inside or outside a building and in a movingvehicle. The UEs may transmit uplink transmissions to the base station.The technology described herein may be implemented in a receiver of anyUE such as the one described in 102 or in the receiver of a base station(BS).

An example system with OTFS modulation parameters described below isconsidered in this document. In the example system, Nv is 512 and Nh is16. In some embodiments, a 2D spreading sequence, or basis, may also bea part of this design. When Nv=512 and Nh=16, there will be 512×16 QAMsymbols (8K) per OTFS frame. Based on the QAM level (BPSK, QPSK, 16QAM,64QAM, . . . ) level, an OTFS frame would carry 8K, 16K, 32K . . . bitsof information. In the example implementation, channel bandwidth (BW)considered in 10 MHz. The number of OTFS frames transmitted isapproximately 1000 frames per second. When the BW doubles, the number offrames per second will double, thus enabling double the throughput. AnOTFS frame in (f, t) domain is padded with appropriate cyclic suffix(CS) and prefix (CP). This will contain (512+CP+CS)×16 complex samples.This frame, after serialization gets transmitted after appropriateup-sampling and up-conversion.

Some terms and their example embodiments are described next.

OTFS frame or OTFS symbol or Data frame—OTFS modulated (2D modulation)data symbol. This frame resides in the (delay, Doppler) also called (τ,ν) domain. In the (τ, ν) domain, OTFS frame is a matrix of QAM symbolsof size (Nv×Nh). Transformation to other domains are possible by meansof Simpletic Fourier Transform which is achieved by Discreet FourierTransform (or Fast Fourier Transform). Conversion from (τ, ν) to(frequency, time) also called (f, t) is achieved by performing a 2D FFTon a frame in (τ, ν). A 1D FFT on a (τ, ν) frame along the Dopplerdimension (Nh) gives rise to a frame in (τ, t) whereas a 1D FFT alongthe delay dimension (Nv) gives rise to a frame in (f, ν).

A sample—At the transmitter, the above OTFS symbols are up-sampled andfed to the ADC. They are called transmit samples. Similarly, thereceived analog signal at the OTFS receiver is sampled and goes througha down conversion process. At different stages of the receiver each QAMsymbol could be constituted by one or more samples.

Pilot Frame—An OTFS frame of pilot symbols. This is a frame constitutedon a sparse grid. In the current system each pilot symbol (512×1 matrixin this realization) is interleaved with a data frame. 16 such pilotsymbols constitute a pilot frame.

Carrier Offset Recovery—the process of detecting carrier phase/frequencyoffset and using a) one-time frequency correction and b) a continuouslytracking phase-locked loop (PLL) to lock the phase and frequency to thetransmit carrier.

Frame Detection/recovery—the process of aligning the receiver to thestart of OTFS frame to the nearest symbol interval.

Timing Offset Recovery—the process of detecting fractional symbol timingphase offset and aligning the receiver sample phase to the most“appropriate” value either by using a PLL or a one-time symbol timingphase correction.

Symbol timing—the process of adjusting the receiver timing phase by afractional (QAM) symbol timing offset. Timing offset recovery and symboltiming are used interchangeably.

Carrier Offset recovery, Frame Detection and symbol Timing recoveryconstitute synchronization.

Pilot auto-correlator—Performs a sample-by-sample linear correlation ofthe received pilot with the subsequent received pilot. Information fromthis is used for carrier frequency offset detection and adjusting theAGC gain.

Matched Filter (MF) correlator—It correlates a received pilot with theoriginal transmitted pilot sequence. Information from this is for symboltiming and absolute carrier phase offset detection.

FIG. 2 shows the multiplexing of data and pilot frames in this example.In the depicted example, pilot signals have been interleaved at regularintervals with data frames (called pilotsymbolSpacing). In someembodiments, 16 sparsely spaced pilot symbols may constitute a pilotframe. The system may also use a schedule and data frames can be presentor absent as per the schedule.

FIG. 3 shows an example structure of a pilot symbol. Time is representedon the horizontal axis. A pilot frame may comprise a cyclic prefix (CP),followed by a pre-defined signal field, followed by a cyclic suffix(CS). The signal field may include an impulse function, and at thereceiving side, the corresponding received signal may thereforerepresent channel response.

FIG. 4 shows an example structure of a data frame. The horizontal axisrepresents time. Each data frame may include a CP, followed by a datasegment, followed by a CS. For example, data segment may be Nv symbolsin duration, where Nv=512. A data frame may include Nh number of datasegments. For example, Nh=16 may be used in some implementations.

FIG. 5 is a block diagram of an example implementation of receiversynchronization. It shows interconnections of sub-blocks that constitutesynchronization system. It also describes how the synchronization systemis connected to the receiver front end (FE). The label ADI refers toAnalog Devices company which sells Integrated Circuits that implementthe FE, shown as an example implementation in FIG. 5. In the depictedexample, a receiver design with two receive antennas is depicted. The RFsignal received at each antenna may be processed through analog todigital conversion stage (ADC) in which the signal may be down-convertedinto baseband signal using a local Voltage Controlled Oscillator (VCO)or a local Voltage Controlled Crystal Oscillator (VCXO). The basebandsignal may be converted from analog to digital domain and digitaldecimation filtering may be performed on the signal to band limit thesignal. The resulting digital signal may be processed through a syncblock in which various correlator blocks may be used to perform phaseand frequency estimation/detection, as described in the presentdisclosure. The output of the sync and detection operations may be acarrier frequency or phase error estimate and a symbol timing offset(STO) estimate. These signals may be used to generate feedbackinformation using which the local VCXO may be adjusted and fractionalsymbol timing adjustment may be made to improve synchronization. Varioussub-blocks in the synchronization are depicted in this diagram and theyare described in the sequel. As shown in the diagram, in this exampleimplementation, many of the synchronization blocks are implemented inField Programmable Gate Arrays (FPGA). A few blocks and control signals,however, are implemented using a processor such as micro-blaze. Thesedetails are shown in FIG. 7 and described in detail in the sequel.Broadly speaking the control signals control the VCXO output, FE gain,decimation filter settings and provides error signal to a phase-lockedloop (PLL) for frequency tracking.

FIG. 2 also shows an example of forming correlation between adjacentpilot transmissions. This technique may include correlating adjacentpilot transmissions (as shown by arrows that logically connect adjacentpilot transmissions). In some embodiments, autocorrelation may beperformed at first on the received signal; following this a movingaverage filter of the appropriate size may be used to obtain single andclean correlation peaks irrespective of the underlying channel and noiseconditions. These correlation peaks may be used to correct an initialfrequency offset, coarse and fine automatic gain control (AGC) andfrequency tracking using a PLL. The peak information may includeinformation about peak position, I/Q amplitude and I/Q angle. In someembodiments, a correlator that correlates CP and/or CS signals may beused for faster frequency convergence.

Using auto-correlation of pilot transmissions instead of otheralternatives (e.g., matched filtering) may provide operational benefitsfor a variety of reasons. For example, pilots are often used for channelsounding and so they are packed such that pilot frame spacing isrelatively short in comparison to channel time variability. Therefore,coherence time, even under high Doppler situations, is typically higherthan pilot frame spacing. In other words, relative variations betweenadjacent pilot transmissions will not be significant, even under highDoppler conditions. Therefore, a pilot auto-correlation sequence will beless susceptible to Doppler induced channel changes, and thus it may berelatively easy to discern peaks in the auto-correlation, even underextreme Doppler conditions and carrier frequency offsets. On the otherhand, a matched filter based auto-correlation peak generation schemetypically matches the received pilot signals with a fixed sequence, andoften is susceptible to Doppler and frequency offsets. Therefore, insome embodiments, matched filtering may be unsuitable for frequencyoffset determination.

In some embodiments, data frames may be scrambled so as to eliminateunwanted data correlations, which may lead to false correlation peaks.

FIG. 6 shows a flowchart for signal processing performed in one exampleimplementation of receiver synchronization and AGC algorithms. Note thatsynchronization and AGC are intertwined and they are adjusted in aprogressive manner. Initially the receiver gain is adjusted to obtain areasonable receive signal quality, followed by a gross correction ofcarrier and clock offsets, followed by more accurate adjustments of AGCand synchronization in several stages. Briefly, the following steps maybe performed.

Step 1: initial gain adjustment. At the beginning, the modem receivertypically has no idea about the strength of the input signal. It may betoo weak or too strong. So set the initial AGC gain to the maximumpossible. If the incoming signal is too strong, it will cause clippingin the Analog to Digital converter. As a first step, sense the clippingand perform gross adjustment of the receiver front end gain such thatsignal after gain stabilization is not clipping any more. In someembodiments, the initial gain adjustment may be performed using coderunning on a microprocessor.

Step 2: coarse gain adjustment. Pilot autocorrelation peaks can be usedto estimate the incoming signal power at this stage (taking the averageamplitude of the correlation peaks and use a look up table). However,this estimation need not be very accurate as the signal peak amplitudesthemselves will be highly varying if there is high carrier frequencyoffset or Doppler offset. Hence this power estimation and associatedgain adjustment will be coarse. In some embodiments, the coarse gainadjustment may be performed using a combination of code and FPGA.

Step 3: using the pilot autocorrelation peak positions estimate thegross carrier frequency error. It is customary that the carrierfrequency and QAM symbol frequency are derived from the same crystaloscillator (frequency synthesizer/VCO) at the transmitter and receiver.If there is a big carrier frequency offset, there will be a proportionalclock frequency (symbol clock) offset as well. This will get manifestedas a gradual drift in the pilot auto-correlation peak positions.Collecting the auto-correlation peak positions over a long period (forexample 1000 peak positions) and performing a linear fit as described inthe sequel can give a good estimate of the symbol frequency drift.Oscillator frequency offsets as high as 20 to 25 parts per million (ppm)can be estimated using this method. After the estimation, thecorresponding correction is applied to the VCXO at the receiver. Oncethe correction has taken effect, the expected frequency offset is muchsmaller. In this example realization, this could be typically less than2 ppm. At this stage, correlation peaks will be well stabilized and theycan be used for a) a more accurate estimate of the received power and b)OTFS Frame start.

Step 4: Coarse carrier frequency offset (CFO) correction. Correlatingsamples that are 512 samples apart (in this example realization) willproduce 16 distinct peaks for the data frame and I peak for the pilotsub-frame. Each of these peaks can be used to compute the remainingfrequency offset as described later. This is called the coarse frequencyoffset. Once estimated, it can be corrected at the VCXO.

Step 5: Determination of coarse frame start (frame start detection).Correlation peak index may be used for this step.

Step 6: Fine frame start estimation is performed using demodulatedchannel peak index.

Step 7: Symbol timing offset (STO) estimation. This may be performedusing interpolation of the channel response amplitude as described indetail later.

Step 8a: At this stage, the carrier frequency offset is expected to be afew Hz, frame detection is functional and the symbol timing offset isinsignificant. From the phase values of the pilot-auto-correlationpeaks, the frequency error can be estimated. Note that these errorvalues are obtained at regular intervals (in this case—1 ms). This isfed into a 2nd order PLL, which tracks the carrier frequency offset atthe receiver.

Step 8b: Receiver Gain need to be tracked to account for the slowvarying power variations. This can be achieved by computing the receivedpower from the pilot auto-correlation peaks.

In some embodiments, Steps 1-8 could be performed partly in hardwarecircuits and partly using one or several microprocessors. FIG. 7 shows ablock diagram of an example implementation of synchronizationhighlighting the top level control aspects. FIG. 5 shows some exemplaryfunctions used by the synchronization sub-system. As shown in FIG. 7,the synchronization output (New Sync) includes a frame start indicationto the OTFS demodulator, a peak power and peak frequency indication tothe microprocessor and the loop filter respectively and a carrierfrequency offset (CFO) measurement to the feedback signal for adjustingthe oscillator.

FIG. 8 shows an example implementation of the Front end receiveprocessing chain. From left to right in FIG. 8, a received signal may beamplified through low noise amplifier (LNA) and down converted using amixer. Mixer output may be processed through a trans-impedanceamplifier. This signal is processed through a lowpass filter to filterout-of-band signal. The low pass filtered signal is digitized by ananalog to digital convertor (ADC). As described earlier, initially,since the signal strength is unknown, the receiver front end is set tomaximum gain. If the input is not very weak, this gain can causeclipping at the ADC. A peak overload detector may detect peaks at thedigital output. Looking at the clip statistics, initial AGC gain need tobe adjusted (one or more times) such that signal at the output of theADC is not clipped.

The digital signal from ADC may be filtered through a half-band filterand down sampled. A digital gain correction mechanism may also be partof the FE. The resulting signal may output to a port for furtherprocessing such as pilot autocorrelation detection. As further shown inFIG. 8, the output of the ADC may be used for the peak overloaddetection and the output of the halfband filter may be used for lowaverage power detection. A digital multiplicand is used to generatedsignal for peal detection.

FIG. 9 highlights Step 2 (coarse gain control of AGC) of FIG. 6. Thecircuit shown in FIG. 9 may be configured to operate at a receiver gainsuch that the received signal is within a few dB to the referencesignal. In this scenario, pilot autocorrelation peak amplitude may beproportional to the received power. This may be used to perform coarsegain control that includes a decision to increment or decrement thegain.

The average power of a sequence, represented as r(n) may be written as:

$\begin{matrix}{{P(n)} = {\frac{1}{N}\Sigma_{1}^{N}{r(n)}*{r^{*}(n)}}} & {{Eq}.\mspace{14mu} (1)}\end{matrix}$

If the signal doesn't undergo much change in N samples, one canapproximate r(n) r(n−N). Thus Eq. (1) may be re-written as

$\begin{matrix}{{P(n)} = {\frac{1}{N}\Sigma_{1}^{N}{r(n)}*{r^{*}\left( {n - N} \right)}}} & {{Eq}.\mspace{14mu} (2)}\end{matrix}$

Eq. (2), therefore, maps correlation peak amplitude to received signalpower.

FIG. 10 shows an example of a circuit for calculating pilotautocorrelation using the above-described equations. The box “Z”represents a unit delay in the digital domain. The output of the abovecircuit can be written as:

b(n)=Σ_(k=n−511) ^(n) r(k)×r*(k−N)  Eq. (3)

The value of 512 unit delay is used as an example only.

FIG. 11 shows an example of waveforms after auto-correlation and movingaverage (MA) filtering. As shown in FIG. 11, for example, theautocorrelation peak for pilot detection may not be a single peak butmay be a flat top curve, with the width of the flat top representingambiguity of the exact location of the pilot obtained from theautocorrelation based coarse granularity detection. The width of theflat top is a function of the channel length; lower the channel length,higher the width. FIG. 11 highlights the fact that correctly designed MAfilter entails a single peak irrespective of the underlying channel.

FIG. 12 shows an example of a peak detection moving average filter andits frequency response. In the drawing, the output c(n) may berepresented as:

c(n)=Σ_(k=n−L+1) ^(n) b(k)  Eq. (4)

The parameter L may be a programmable parameter. For example, L may beselected to be equal to the number of samples in CP and CS combined.This choice of L always avoid flat top for correlation peaks at allchannel conditions. MA filter filters amplitude swings due to noise, byaveraging over L samples.

FIG. 13 is a graph of an example output of an autocorrelator implementedaccording to the technique described with respect to Eq. 1 to Eq. 4. Theperiodicity of the peaks approximately corresponds to the periodicity ofpilot signal transmissions. The heights of the peaks are alsoapproximately proportional to the received signal power.

FIG. 14 shows a zoomed view of an example graph of output of anautocorrelator around one of the peaks. As previously described, theflatness of the peaks may represent the inaccuracy or ambiguity in thedetermination of the exact pilot locations during the coarse estimationstage.

FIG. 15 shows a zoomed view of an example graph of a moving averageoutput around one of the peaks. Using MA filter over the waveform shownin FIG. 15, outputs that have sharper peaks can be obtained. Thus, MAfiltering can provide a more accurate and fine granular location ofpilot transmissions.

FIG. 16 is a graphical example of successive peaks of a peak detectioncircuit. In the graph, the horizontal axis represents time or inputsample index and the vertical axis represents correlation amplitude atthe output of the MA filter after low pass filtering, computed as givenby Eq (5a). Positive slope of the curve indicates that samples areincreasing for G1 consecutive samples (ignoring small deviations). Anegative slope is similarly detected if G2 consecutive samples aredecreasing in amplitude. A peak is declared detected, if a positiveslope followed by a negative slope is identified. For robust peakdetection, the peak must be well above the noise floor. In this case, nothreshold adjustment is needed to be done on the fly.

E(n)=E(n−1)·α+(1−α)·b(n)·b*(n)  Eq. (5a)

In the above equation, b(n) represents value at time n, a is anexponential window factor. It is a real number between 0 and 1, and E(n)represents power estimate at time n.

Initial Coarse Carrier Frequency Offset (CFO) Estimation and Correction.

Transmit & Receive VCOs are free running oscillators. Even if both theVCOs are set to identical operating points, the frequency deviationbetween them could be significant. For example, these oscillators canhave a deviation as high as 22 ppm. For synchronization at the receiver,both these VCOs need be synchronized. Objective of the initial Frequencyadjustment is to bring down this difference to less than a tenth of theoriginal offset, for example 2 ppm. In some implementations, the symbolclock (e.g. 10 MHz) and carrier frequency (e.g., 3.6 GHz) are derivedfrom the same VCO clock (e.g. 40 MHz). By synchronizing the VCOs ofTransmitter and Receiver, symbol clock and carrier frequencysynchronization can be achieved. A large VCO frequency offset willresult in a symbol clock offset that is measurable in a fairly largeobservation window (e.g., 1 second).

With reference to FIG. 18, the following equations can be written forthe horizontal and vertical axes, where x_(i) represents consecutivepilot number (1, 2, . . . ) and y_(i) represents the correspondingsymbol clock count for the correlation peak for i-th pilot. In anexample implementation, peak detection and the corresponding symbolclock count can be realized in hardware. If there is no clock drift, aline can be drawn connecting these points and the slop of that line willbe equal to the pilot frame spacing (in the given figure it is 10856symbol clock counts). However, when the transmitter and receiver clocksare not synchronized, these points need not be lying on a line. A linecan be fitted in the lest square sense as described below.

(x _(i) ,y _(i)) for i=1,2, . . . ,N  Eq. (5b)

The slope can be expressed as

$\begin{matrix}{{{slope} = \frac{{Ns}_{xy} - {s_{x}s_{y}}}{{Ns}_{xx} - s_{x}^{2}}}{Where}} & {{Eq}.\mspace{14mu} (6)} \\{{s_{x} = {\sum\limits_{i = 1}^{N}\; x_{i}}},{s_{y} = {\sum\limits_{i = 1}^{N}\; y_{i}}},{s_{xy} = {\sum\limits_{i = 1}^{N}\; {x_{i}y_{i}}}},{s_{xx} = {\sum\limits_{i = 1}^{N}\; x_{i}^{2}}}} & {{Eq}.\mspace{14mu} (7)}\end{matrix}$

Let t_(pilot) denote the time interval between 2 consecutive pilotsub-frames, f_(exp) denote the number of symbol clock cycles per secondwhen the transmitter and receiver are synchronized and Δf_(c) denotesthe symbol clock frequency deviation between the transmitter andreceiver, then it can be seen that λf_(c) is given by,

$\begin{matrix}{{\Delta \; f_{c}} = {\frac{slope}{t_{pilot}} - {f_{\exp}.}}} & {{Eq}.\mspace{14mu} (8)}\end{matrix}$

FIG. 17 shows an example graph of a linear fit for pilot autocorrelationpeaks. This symbol frequency error estimate can appropriately be scaledto obtain and correct the initial coarse VCO error.

In some implementations, the carrier frequency may nominally be a largemultiple of VCO frequency (e.g., 90 times the VCO frequency). Due tothis multiplicative factor, any ‘mis-alignment’ in the VCO frequencywill results in high carrier frequency offset. In some embodiments, theinitial frequency estimation is based on VCO frequency drift measurement(as described in the previous section). The inaccuracy could stillresult in a fairly high carrier frequency offset. However, it has beenfound empirically that the above VCO estimation and correction usuallyresults in a residual carrier offset error that is much less than halfthe sub-carrier width (for example <˜9 KHz (½*1/512*1/(10*10{circumflexover ( )}6) for 10M symbols/second transmission)). Coarse CFOestimation/correction may be applied further, to bring down the carrierfrequency offset to sub 100 Hz range.

Coarse Frequency Estimation and Correction

FIG. 18 shows an example of a coarse frequency estimation scheme. Inputsignal is passed through a CP/CS correlator to produce a sequence a(n)(e.g., as described with respect to FIG. 10, e.g., signal b(n) in thisfigure). The resulting signal is then filtered through a moving averagefilter as described in FIG. 12. The peaks of the output are detectedusing a peak detector (for example, as described with respect to FIG.16). In this example embodiment, there will be 17 such peaks, 16 of themcorresponding to the OTFS data frame and one for the pilot sub-frame.Peaks that correspond to the data frame occur with equal distancebetween peaks. However, the distance between pilot-peak and data peakneed not be equal to this. The coarse frequency estimator takes thelocation of each of the 16 data peaks (corresponding to each antenna),computes the phase of each peak and uses this information to determine acoarse carrier frequency offset estimate (more details below).

FIG. 19 shows an example of calculating correlation in transmissionswith cyclic prefix. To provide a cyclic extension of the transformeddata, the end of the data may be inserted before the beginning as acyclic prefix (CP) and the beginning of the data is inserted after theend as a cyclic suffix (CS). Performing correlations spaced by 512samples gives a peak for each of 16 data columns and one pilot column ina frame. In some embodiments, the scheme depicted in FIG. 19 provides 17phase measurements in one frame with no change to signaling structures.

FIG. 20 shows an example of a correlation calculation scheme, wherein“conj” refers to the conjugate operation.

In FIG. 20, the following relation holds:

a(n)=Σ=_(k=n−CP−CS+1) ^(n) r(k)×r*(k−N)  Eq. (9)

The value N=512 may be used in some implementations. In someembodiments, the circuit shown in FIG. 20 may be used to calculate theautocorrelation between CS and data.

FIG. 21 illustrates how single peaks can be achieved (independent of theunderlying channel) for CP/CS correlator with MA filtering. In thefigure, each peak's phase gives CFO estimate as

$\begin{matrix}{{\Delta \; f_{c}} = {\frac{1}{2\pi \; {NT}_{S}}\Delta \; {peak}}} & {{Eq}.\mspace{14mu} (10)}\end{matrix}$

In some embodiments, Δf_(c) from multiple peaks may be averaged to get abetter coarse CFO estimate. Further, Δf_(c) computed from differentreceive chains can be averaged for better noise immunity. This carrierfrequency error estimate can appropriately be scaled to obtain andcorrect the coarse VCO error.

Coarse Frame Boundary Detection

FIG. 22 shows an example scheme for coarse frame boundary detectionusing pilot autocorrelation peak. The scheme may be used to identify thebeginning of Pilot frame and an OTFS data frame for subsequentprocessing (e.g., channel estimation, equalization, and datademodulation). The locations of the pilot correlation peaks indicate thebeginning of pilot frames, with a constant, deterministic offset. In thedrawing, the pilot to data (P2D) offset is shown to be a fixed number.Therefore, a coarse estimate of the frame start may be based oncorrelation peak detection by adding the value of P2D spacing, which isknown a priori to the receiver.

Correlation peaks, in general, may not be a very accurate metric. Frameboundary detection based on this technic may still be coarse. Theboundary detected can be off by a few samples. This may not be acritical issue because the existence of CP protects against data loss.

Fine Frame Boundary Detection

FIG. 23 shows an example scheme for fine frame boundary detection usingdemodulated channel response. As previously described, correlation peakspositions are not a very accurate metric for frame boundary detection.To overcome this issue, in some embodiments, the frame start may befinely aligned so as to coincide with the peak of the channel impulseresponse. Demodulation of pilot frame (that was coarsely located usingcorrelation peak position technique described earlier) and finding themaximum value of the impulse response magnitude are shown in FIG. 23. Insome embodiments, resynchronization may be performed when tracked valuebecomes significantly different from the original value.

Fractional Offset Delay Estimation and Correction

In general, channel impulse response can be such that the peak of theimpulse response does not coincide with a sample boundary. To align thepeak with a sample boundary, a fractional offset adjustment of thereceived samples should be performed. Once the phase offset it detected,the correction can be implemented with an appropriately sampledpoly-phase filter. There are two mechanisms to determine this phaseoffset error.

One mechanism may be to use interpolation of the channel using sincfunction and determining the peak and thus the fractional offset. Theother mechanism is to perform band edge timing recovery (e.g., see FIG.24). It may be possible that even if this correction is not applied, theMISE feed forward equalizer will correct for this discrepancy. Theabsence of a fractional offset correction may result in higher impulseresponse tail which will entail the use of a longer cyclic prefix.

STO Error Estimation and STO Correction

FIG. 35 illustrates the principle of fractional timing offset errorestimation using sync interpolation. After the pilots are demodulated,let S2 be the output of the peak pilot amplitude. Let the samples thatare adjacent to S2 be S1 and S3 as shown in FIG. 35. A magnitudeinterpolation could provide an estimate of the fractional timingestimate error as shown in the figure. Note that normalizing S1 and S3with magnitude of S1 can make the interpolation more accurate and easyto implement. In some embodiments, the normalizations and amplitudecalculations can be performed using cordic algorithm.

FIG. 24 shows a flowchart for symbol timing offset correction. Once thesymbol timing offset error is detected from the demodulated pilots, thecorrected value can be computed as below: First, add the error to thecurrent offset value and check if the computed offset is above a setthreshold. If the computed offset value is above the threshold, furthercheck if the offset is bigger than sample spacing. If the computedoffset is more than the sample spacing, the final fractional offset tobe applied is obtained through a modulo operation (e.g., subtract offone sample offset value). The number of integer samples, in this case,may be corrected separately.

FIG. 25 shows an example scheme for band-edge timing recovery. In thisscheme, normalized, band-pass filtered received baseband waveform iscorrelated to get an estimate of the timing phase. The STO estimator isapplied only during the receive data frames (e.g., not duringzero-energy guard or transmit intervals).

FIG. 26 shows examples of band-pass filters that may be used in thescheme described in FIG. 25. These bandpass filters may have thefollowing transfer functions. Note that 0<b<1. The frequency responsesof these filters are given in the same figure.

$\begin{matrix}{{H_{p}(z)} = \frac{b}{1 + {\left( {1 - b} \right)e^{\frac{j*\pi}{2}}z^{- 1}}}} & {{Eq}.\mspace{14mu} (11)}\end{matrix}$

Similarly

$\begin{matrix}{{H_{n}(z)} = \frac{b}{1 + {\left( {1 - b} \right)e^{\frac{{- j}*\pi}{2}}z^{- 1}}}} & {{Eq}.\mspace{14mu} (12)}\end{matrix}$

PLL Tracking

In some examples, the angle between I and Q of the auto-correlationpeaks is proportional to the carrier frequency error (if the frequencyerror is less than ½ the rate of pilot occurrence). In some examples,after the coarse frequency correction, the residual frequency error ismuch less than this. Driving a 2^(nd) order PLL using this error signalhelps to track the carrier frequency. PLL error sampling frequency (therate of pilot occurrence) is the inverse of pilot Frame Spacing. This isgenerally, very small compared to the sampling rate of the incomingdata. This constrains the bandwidth of the PLL to be generally narrow.Additionally the correction has to be applied at the VCO level (at amuch lower frequency than the carrier frequency). This reduces theapplicable BW further. This causes the PLL to convergence slowly. As aresult, the instantaneous frequency deviations due to Doppler cannot beeffectively tracked.

A mathematical model for the auto-correlation of the received pilotsignal may be developed as follows:

Denote pilot signals by p. Let channel have N taps with delay, Dopplerand attenuation τ_(i), ν_(i), α_(i). Let the time between pilot columnsbe denoted as T Pilot received at kT may be represented as:

$\begin{matrix}{\sum\limits_{n = 1}^{N}\; {a_{n}{\exp \left( {2\pi \; {iv}_{n}{kT}} \right)}{\exp \left( {2\pi \; {{iv}_{n}\left( {t - \tau_{n}} \right)}} \right)}{p\left( {t - \tau_{n}} \right)}}} & {{Eq}.\mspace{14mu} (13)}\end{matrix}$

Similarly, pilot received at (k+1)T may be represented as:

$\begin{matrix}{\sum\limits_{n = 1}^{N}\; {a_{n}{\exp \left( {2\pi \; {{iv}_{n}\left( {k + 1} \right)}T} \right)}{\exp \left( {2\pi \; {{iv}_{n}\left( {t - \tau_{n}} \right)}} \right)}{p\left( {t - \tau_{n}} \right)}}} & {{Eq}.\mspace{14mu} (14)}\end{matrix}$

Correlating signals at kT and (k+1)T is obtained by taking the innerproduct of the above 2 terms. Mathematically,

Corr_(n,m)=

exp(2πiν _(n)(t−τ _(n)))p(t−τ _(n)),exp(2πiν _(m)(t−τ _(m)))p(t−τ _(m))

The above operation will result in 2 terms as below. The first term is asinusoid that has oscillation frequency equal to the Doppler frequency.The amplitude of this term is a function of the channel magnitude (an).When the system is not carrier locked, the Doppler frequency willencompass the carrier frequency offset as well. However, when thecarrier frequency offset is nil, this term will represent the Dopplerfrequency offset between the transmitter and receiver. The 2^(nd) termis proportional to the Doppler frequency beats. Usually these beatfrequencies will be quite low compared to the Doppler frequency in thesystem.

$\begin{matrix}{{\sum\limits_{n = 1}^{N}\; {{a_{n}}^{2}{\exp \left( {2\pi \; {iv}_{n}T} \right)}}} + {2{\sum\limits_{n = 1}^{N}\; {\sum\limits_{m = {n + 1}}^{N}\; {{{real}\left( {a_{n}{\overset{\_}{a}}_{m}} \right)}{\cos \left( {2{\pi \left( {v_{n} - v_{m}} \right)}{kT}} \right)}{corr}_{n,m}}}}}} & {{Eq}.\mspace{14mu} (15)}\end{matrix}$

The beats (the second term in Eq. 15), may be removed using a low passfilter. This low-passed filtered error signal is sent to the PLL fortracking. In order to obtain a constant amplitude error signal, it isrecommended to obtain the phase of the peaks of this signal and computethe equivalent frequency error and use it to drive the PLL.

PLL Tracking Using Pilot Autocorrelation Peaks

FIG. 27 shows an example scheme for phase-locked loop tracking usingautocorrelation peaks. The RF input is sampled using a sample clock.Pilot autocorrelation is performed on digitized signal. The output ofautocorrelator is filtered through a low pass filter. This is followedby a peak phase detection circuit. The detected phase peaks areconverted into frequency values using a phase to frequency conversionand the result is fed back through a loop filter and a digital to analogconversion circuit to adjust the sampling clock generated by a voltagecontrolled oscillator (VCO).

AGC Gain Adjustment

FIG. 28 shows an example of AGC gain adjustment using pilotautocorrelation peak amplitude and multiplier. As described earlier, aninitial gain adjustment is estimated using the ADC clip statistics. Thecoarse gain adjustment is estimated by measuring the pilotauto-correlation peak amplitude. This estimation, as was disclosedearlier, will be coarse as the auto-correlation peak amplitude can bevarying significantly due to the high initial carrier frequency offsetthat may exist. Once the carrier PLL is in the tracking mode, pilotauto-correlation peaks will be relatively stable and their amplitude canbe used for estimating the AGC fine gain. It has been found that valuesestimated thus by averaging a few peak amplitudes, could be as close as0.25 dB or better with the true power.

FIG. 29A, FIG. 29B and FIG. 29C show different portions of a flowchartfor an example implementation of AGC and synchronization in an exampleOTFS receiver. All various steps in this flow diagram have beenexplained in the document.

The process depicted in FIG. 29A is as follows. Initially, gain is setat a certain value. Then, clipping of the signal is monitored. The gainis adjusted downwards if clipping is observed. Next, if signal is notbeing clipped, then the AGC and peak detector thresholds are adjusted.Then, n1 number of autocorrelation peaks may be calculated (n1 is aninteger and may be programmable). In some embodiments, the peakcalculation may be performed by a hardware circuit. Next, adetermination may be made about whether peaks exhibit a regularinterval. If not, then the AGC and peak detector thresholds arere-adjusted. If the autocorrelation peaks are occurring at regularintervals, then initial frequency estimation and correction and coarseframe boundary detection are performed.

As depicted in FIG. 29B, after the initial frequency estimation andcorrection is completed, then a coarse AGC gain may be computed using n2number of pilot correlation peaks. Here, n2 is an integer and may beprogrammable. After AGC correction, the CP/CS correlation computationmay be performed. Then n2 autocorrelation peaks are calculated. Thisprocess is iteratively repeated until it is determined that thecorrelation peaks are occurring at a regular interval. Next, a coarsefrequency offset and a VCO correction may be performed.

As depicted in FIG. 29C, after the method of FIG. 29B is performed, fineframe alignment may be performed by computing channel peak and STOerror, followed by alignment of STO, computing and applying fine AGCparameters from autocorrelation peak amplitudes. Next, carrier frequencyand AGC may be iteratively tracked to maintain accuracy during theoperation.

Example of Auto Correlator Hardware Implementation

FIG. 30 shows an example hardware implementation of a correlator that isused embodiments of the disclosed technology. Note that by changingdifferent parameters in this correlator, it can be used both for pilotauto-correlation and CP/CS auto-correlation.

Peak Detection State Machine

FIG. 31 shows an example state machine implementation of a peak detectordescribed in FIG. 16. Here DV stands for Data Valid. In is the inputsample amplitude. P stands for present peak amplitude, F is a scalefactor, and V is the present valley amplitude.

FIG. 32 is a flowchart for an example method 3200 of receiving wirelesssignals. The method 3200 includes receiving (3202) an orthogonal timefrequency space (OTFS) modulated wireless signal comprising datatransmissions interspersed with pilot signal transmissions. For example,for OTFS signals that are defined in the delay-Doppler domain, the pilotsignal transmissions and data transmissions may be multiplexed with eachother along the delay and/or Doppler dimensions. When converted into atime-frequency domain signal and transmitted, the data and pilottransmissions may appear to be overlapping, but may be separable usingthe delay-doppler domain representation.

In some embodiments, the method 3200 includes calculating (3204)autocorrelation of the wireless signal using the wireless signal and adelayed version of the wireless signal that is delayed by apre-determined delay, thereby generating an autocorrelation output. Forexample, the autocorrelation may be calculated by using a number ofvalues of the pre-determined delay to obtain a functional relationshipbetween amount of autocorrelation as a function of the delay. The method3200 further includes processing (3206) the autocorrelation filter byapplying a moving average filter to produce a fine timing signal. Forexample, the timing signal may be used to produce a cleanautocorrelation peak signal independent of the channel response (forexample, as discussed with respect to FIG. 35) and using this performing(3208) synchronization with the wireless signal using the fine timingsignal. During synchronization achieved in 3208, coarse AGC and fine AGCmay also be performed. For example, some techniques are described withrespect to FIG. 6, FIG. 18, FIG. 29A and FIG. 29B.

In some implementations, the method 3200 further includes identifyingframe boundaries for data transmissions based on the autocorrelationoutput. For example, FIG. 11 and FIGS. 12-16 depict and describe movingaverage based techniques. In some implementations, gain adjustment andtracking may also be performed by determining magnitudes of correlationpeaks. For example, FIGS. 18 to 23 and corresponding descriptionsprovide some implementation examples. For example, as depicted in FIG.20, in some embodiments, the moving average filter operates over anumber of samples equal to sum of lengths of cyclic prefix and cyclicsuffix field. The values of the length of CP and CS may be known to thereceiver without using signal processing. For example, these values mayhave been signaled in a message by the transmitter or may bepre-specified by interoperability standard.

In some embodiments, the method 3200 may further include determiningmagnitudes of autocorrelation peaks. This determination may be performedusing a curve representative of the magnitude of autocorrelation as afunction of time delay. For example, FIGS. 14-16 and the correspondingdescription provide some examples of how this technique may beimplemented. In some examples, an exponential weighting may be used forimproving robustness of the calculation. For example, Eq. (5a) and thecorresponding description show one example formula that may be used forthe calculation.

In some implementations, frame boundaries may be identified using alinear fit operation in which a linear approximation on theautocorrelation output. For example, FIG. 6 and the correspondingdescription provides some example implementations of how a linearapproximation is determined for the calculations.

FIG. 33 shows a flowchart for an example method 3300 for receiving anOTFS signals and performing automatic gain control thereof. The method3300 may be performed at a receiver of OTFS signal. Using the method3300, the receiver may successfully receive information bits that aremodulated on the OTFS signal and recover the information bits after thesteps described with respect to the method 3300. As previously discussedwith respect to method 3200, the OTFS signal may include datatransmissions interspersed with pilot signal transmissions.

The method 3300 includes receiving an OTFS modulated signal (3302),performing initial AGC correction (3306; e.g., Step 1 of FIG. 6) usingclip statistics gathered from ADC (3304), followed by a coarse AGCcorrection (3310; e.g., Step 2 of FIG. 6).

In some embodiments, the method 3300 may further include determiningmagnitudes of autocorrelation peaks, and performing a gain adjustmentbased on the determined magnitudes of autocorrelation peaks. Asdescribed with respect to method 3200 and in the present document, anexponential windowing function may be used for the calculation of theautocorrelation peaks. In some embodiments, magnitudes of theautocorrelation peaks may be used to determine a symbol timing offsetvalue for symbols in the OTFS modulated wireless signal.

In some implementations of method 3200 or 3300, an estimate of thecarrier frequency in the OTFS signal that is received may be obtainedand tracked using a phase-locked loop using the autocorrelation output.Some example embodiments are discussed with reference to FIG. 6, FIG. 7,FIGS. 29A and 29B.

In some embodiments, another phase-locked loop may be used to trackvalues of the gain adjustment that is used during the method 3300 torecover or extract information bits from the OTFS signal. The symboltiming offset value is calculated based on interpolating channelamplitude estimates or by performing band-edge timing recovery. Exampleembodiments are described with reference to FIG. 24 and FIG. 25.

FIG. 34 shows an example of a wireless transceiver apparatus 3400. Theapparatus 3400 includes a processor 3402 that may be configured to readinstructions from a memory 3404 and implements techniques described inthe present document. The apparatus 3400 also includes transceivercircuitry 3406 that is used for receiving and processing wirelesssignals, and may also be used for transmitting wireless signals. Theapparatus 3400 may implement the various signal reception andsynchronization techniques described herein. For example, the OTFSsignals may be received by the transceiver circuitry 3406 which may alsoimplement various operations such as PLL, AGC, and so on.

It will be appreciated that techniques for achieving accuratesynchronization in OTFS receiver are disclosed. In one advantageousaspect, the disclosed techniques are robust to Doppler shifts in thereceived signals. In another advantageous aspect, successively finersynchronization and AGC is achieved using auto-correlation followed by amoving average.

The disclosed and other embodiments, modules and the functionaloperations described in this document can be implemented in digitalelectronic circuitry, or in computer software, firmware, or hardware,including the structures disclosed in this document and their structuralequivalents, or in combinations of one or more of them. The disclosedand other embodiments can be implemented as one or more computer programproducts, i.e., one or more modules of computer program instructionsencoded on a computer readable medium for execution by, or to controlthe operation of, data processing apparatus. The computer readablemedium can be a machine-readable storage device, a machine-readablestorage substrate, a memory device, a composition of matter effecting amachine-readable propagated signal, or a combination of one or morethem. The term “data processing apparatus” encompasses all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this document can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general (for e.g., T2080) and special purposemicroprocessors (for e.g., MicroBlaze or ARC), and any one or moreprocessors of any kind of digital computer. Generally, a processor willreceive instructions and data from a read only memory or a random accessmemory or both. The essential elements of a computer are a processor forperforming instructions and one or more memory devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., EPROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

While this patent document contains many specifics, these should not beconstrued as limitations on the scope of an invention that is claimed orof what may be claimed, but rather as descriptions of features specificto particular embodiments. Certain features that are described in thisdocument in the context of separate embodiments can also be implementedin combination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment can also beimplemented in multiple embodiments separately or in any suitablesub-combination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asub-combination or a variation of a sub-combination. Similarly, whileoperations are depicted in the drawings in a particular order, thisshould not be understood as requiring that such operations be performedin the particular order shown or in sequential order, or that allillustrated operations be performed, to achieve desirable results.

Only a few examples and implementations are disclosed. Variations,modifications, and enhancements to the described examples andimplementations and other implementations can be made based on what isdisclosed.

1. A signal reception method, comprising: receiving an orthogonal timefrequency space (OTFS) modulated wireless signal comprising pilot signaltransmissions interspersed with data transmissions; calculatingautocorrelation of the wireless signal using the wireless signal and adelayed version of the wireless signal that is delayed by apre-determined delay, thereby generating an autocorrelation output;processing the autocorrelation filter through a moving average filter toproduce a fine timing signal; and performing synchronization with thewireless signal using the fine timing signal.
 2. The method of claim 1further including: identifying frame boundaries for data transmissionsbased on the autocorrelation output.
 3. The method of claim 1, whereinthe pre-determined delay is equal to time spacing between successivepilot signal transmissions.
 4. The method of claim 1, wherein the movingaverage filter operates over a number of samples equal to sum of lengthsof cyclic prefix and cyclic suffix field.
 5. The method of claim 1,further including: determining magnitudes of autocorrelation peaks, andperforming gain adjustment based on the determined magnitudes ofautocorrelation peaks.
 6. The method of claim 5, wherein theautocorrelation peaks are determining an exponential windowing function.7. The method of claim 2, wherein the identifying operation includesperforming a linear fit operation on the autocorrelation output.
 8. Themethod of claim 1, wherein the method further includes, during theperforming synchronization, determining a symbol timing offset value forsymbols in the OTFS modulated wireless signal. 9-17. (canceled)
 18. Awireless communication device comprising a processor and transceivercircuitry wherein the transceiver circuitry is configured for: receivingan orthogonal time frequency space (OTFS) modulated wireless signalcomprising pilot signal transmissions interspersed with datatransmissions; and wherein the processor is configured for: calculatingautocorrelation of the wireless signal using the wireless signal and adelayed version of the wireless signal that is delayed by apre-determined delay, thereby generating an autocorrelation output;processing the autocorrelation filter through a moving average filter toproduce a fine timing signal; and performing synchronization with thewireless signal using the fine timing signal.
 19. The wirelesscommunication device of claim 18, wherein the processor is furtherconfigured for: identifying frame boundaries for data transmissionsbased on the autocorrelation output.
 20. The wireless communicationdevice of claim 18, wherein the pre-determined delay is equal to timespacing between successive pilot signal transmissions.
 21. The wirelesscommunication device of claim 18, wherein the moving average filteroperates over a number of samples equal to sum of lengths of cyclicprefix and cyclic suffix field.
 22. The wireless communication device ofclaim 18, wherein the processor is further configured for: determiningmagnitudes of autocorrelation peaks, and performing gain adjustmentbased on the determined magnitudes of autocorrelation peaks.
 23. Thewireless communication device of claim 22, wherein the autocorrelationpeaks are determining an exponential windowing function.
 24. Thewireless communication device of claim 19, wherein the identifyingoperation includes performing a linear fit operation on theautocorrelation output.
 25. The wireless communication device of claim18, wherein the processor is further configured to determine, during theperforming synchronization, a symbol timing offset value for symbols inthe OTFS modulated wireless signal.